Researcher in Renewable Energy Systems · Power Electronics · AI-based Optimization
I develop AI-driven energy management strategies for hybrid clean energy systems — from hydrogen-powered maritime vessels to fuel cell hybrid vehicles. My work sits at the intersection of reinforcement learning, power electronics, and real-time hardware validation.
I am a researcher specializing in intelligent energy management for hybrid renewable energy systems. My research develops reinforcement learning algorithms — including Deep RL, Fuzzy-RL hybrids, and policy gradient methods — to optimize energy flows in fuel cell, battery, and hydrogen-powered systems.
During my PhD at Aix-Marseille University, I pioneered Fuzzy-RL approaches for fuel cell hybrid electric vehicles, combining interpretable fuzzy logic with adaptive reinforcement learning for real-time energy dispatch. My work was validated through Hardware-in-the-Loop (HIL) platforms using dSPACE, OPAL-RT, and Typhoon-HIL.
Currently at ENSM, I lead energy management research for the Navire Propre project, developing hydrogen–diesel–battery hybrid propulsion systems for low-carbon maritime vessels. Beyond AI-driven energy management, I focus on shipboard power system modeling and real-time simulation, with Power Hardware-in-the-Loop (PHIL) validation using Imperix platforms.
I am open to research collaborations, academic exchanges, and consulting opportunities in renewable energy systems, AI-based optimization, and maritime decarbonization.